- Title
- Predicting the condensate viscosity near the wellbore by ELM and ANFIS-PSO strategies
- Creator
- Mousazadeh, Fatemeh; Naeem, Mohammad; Daneshfar, Reza; Soulgani, Bahram; Naseri, Maryam
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176639
- Identifier
- vital:15155
- Identifier
-
https://doi.org/10.1016/j.petrol.2021.108708
- Identifier
- ISBN:0920-4105 (ISSN)
- Abstract
- By lowering the pressure beneath the dew point as the result of production in gas condensate (GC) reservoirs, liquid droplets are formed in the borehole zone. Accurate calculation of pressure decline and optimization operations in these reservoirs need to know and predict the specific properties such as liquid viscosity. Empirical models have already been developed to predict this parameter. Due to the peculiar behavior of fluids beneath the dew point pressure (DPP), the prediction of liquid viscosity associates with an error. With the development of machine learning (ML) approaches, studies on fluid properties like other sciences have entered a new phase. In this study, extreme learning machine (ELM) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO) methods applied to this end. Therefore, a large data bank of reservoir and fluid properties including reservoir temperature and pressure, specific gravity (SG) of gas, API gravity, and gas to oil ratio (Rs) were used. The results showed that R-squared and RMSE for ANFIS-PSO are 0.762 and 0.15, respectively, while these values are 0.941 and 0.06 for ELM which shows that the last model has a better performance in estimating output values. Also, the range of reliable data is determined, and further, a sensitivity analysis was done, which showed that the greatest impact on the viscosity was from SG, and API gravity has the least effect on it. This model can be used as a reference for calculating condensate viscosity and also by expanding the range of datasets, it can be applied in the commercial software. © 2021 Elsevier B.V.
- Publisher
- Elsevier B.V.
- Relation
- Journal of Petroleum Science and Engineering Vol. 204, no. (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 Elsevier B.V. All rights reserved.
- Subject
- 0403 Geology; 0904 Chemical Engineering; 0914 Resources Engineering and Extractive Metallurgy; Extreme learning machine; Gas condensate; Machine learning; Particle swarm optimization; Viscosity
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